Integrated Q-Learning with Firefly Algorithm for Transportation Problems


  • K R Pratiba Coimbatore Institute of Technology
  • S Ridhanya Coimbatore Institute of Technology
  • J Ridhisha Coimbatore Institute of Technology
  • P Hemashree Coimbatore Institute of Technology



Q Learning, Firefly Algorithm, Genetic Algorithm, Ant Colony Optimization Algorithm, Particle Swarm Optimization


The study addresses the optimization of land transportation in the context of vehicle routing, a critical aspect of transportation logistics. The specific objectives are to employ various meta-heuristic optimization techniques, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Q-Learning reinforcement algorithm, to find the optimal solutions for vehicle routing problems. The primary aim is to enhance the efficiency and effectiveness of land transportation systems by minimizing factors such as travel distance or time while adhering to constraints. The study evaluates the advantages and limitations of each algorithm and introduces a novel-based approach that integrates Q-learning with the FA. The results demonstrate that these meta-heuristic optimization techniques offer promising solutions for complex vehicle routing challenges. The integrated Q-learning with Firefly Algorithm (iQLFA) emerges as the most successful approach among them, showcasing its potential to significantly improve transportation optimization outcomes.


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Marco Dorigo, Christian Blum. Ant Colony Optimization Theory: A Survey. Theoretical Computer Science. 2005; 344(2-3):243-278 DOI:

David E Goldberg and William Shakespeare. Genetic Algorithms. 2002. DOI:

Vijay Kumar, Dinesh Kumar. A Systematic Review on Firefly Algorithm: Past, Present, and Future. Archives of Computing Methods in Engineering. 2020; 28:3269–3291. DOI:

James J. Q. Yu, Wen Yu, Jiatao Gu. Online Vehicle Routing with Neural Combinatorial Optimization and Deep Reinforcement Learning. IEEE Transactions on Intelligent Transportation Systems. 2019; 20(10):3806-3817. DOI:

Mohamed Ben Ahmed; Farah Zeghal Mansour; Mohamed Haouari. A PSO Approach for Robust Aircraft Routing. In: Proceedings of IEEE International Conference on Industrial Engineering and Engineering Management (IEEM); 06-09 Dec; Singapore: IEEE; 2015 p. 219–223. DOI:

A. L. C. Ottoni, E. Nepomuceno, Marcos Santos de Oliveira, Daniela Carine Ramires de Oliveira. Reinforcement learning for the Traveling Salesman Problem with Refueling. Complex & Intelligent Systems. 2022; 8:2001–2015. DOI:

Sharad Kumbharana. Solving Travelling Salesman Problem Using Firefly Algorithm. International Journal for Research in Science & Advanced Technologies. 2013; 2(2):53-57.

Feng Wu. Contactless Distribution Path Optimization Based on Improved Ant Colony Algorithm. Mathematical Problems in Engineering. 2021; 2021:1-11. DOI:

Robin T. Bye, Magnus Gribbestad, Ramesh Chandra, Ottar L. Osen. A Comparison of GA Crossover and Mutation Methods for the Traveling Salesman Problem. In: Janusz Kacprzyk. Advances in Intelligent Systems and Computing. Proceedings of Innovations in Computational Intelligence and Computer Vision (ICICV 2020); 17-19 Jan; Manipal University, Jaipur. Springer Singapore; 2020. p. 529-542. DOI:

Min-Xia Zhang, Bei Zhang, Yu-Jun Zheng. Bio-Inspired Meta-Heuristics for Emergency Transportation Problems. Algorithms. 2014; 7(1):15-31. DOI:

Anahita Sabagh Nejad, Gabor Fazekas. Solving a Traveling Salesman Problem using Meta-Heuristics. International Journal of Artificial Intelligence. 2022; 11(1):41-49. DOI:

Aigerim Bogyrbayeva, Taehyun Yoon, Hanbum Ko, Sungbin Lim, Hyokun Yun, Changhyun Kwon. A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone. Transportation Research Part C: Emerging Technologies. 2023; 148:103981. DOI:

Youssef Harrath, Abdul Fattah Salman, Abdulla Alqaddoumi, Hesham Hasan, Ahmed Radhi. A Novel Hybrid Approach for Solving the Multiple Traveling Salesmen Problem. Arab Journal of Basic and Applied Sciences. 2019; 26(1):103-112. DOI:

Anubhav Kumar Prasad, Dharm Raj Singh, Pankaj. A Genetic Method Using Hybrid Crossover for Solving Travelling Salesman Problem. International Journal of Recent Technology and Engineering. 2019; 8(2):5066-5072. DOI:

Yu Huang, Xifan Yao, Junjie Jiang. An Improved Firefly Algorithm for Generalized Traveling Salesman Problem. In: Gerhard Goos, Juris Hartmanis. Lecture Notes in Computer Science. Proceedings of Intelligent Computing Theories and Application. August 12–15, 2021, Shenzhen, China. Springer Cham; 2021. p. 739-753. DOI:

James Kennedy and Russell Eberhart. Particle Swarm Optimization. In: ICNN'95 - Proceedings of International Conference on Neural Networks. 27 November 1995 - 01 December; Perth, WA, Australia. IEEE; 1995. p. 1942-1948.

Penggang Gao; Zihan Liu; Zongkai Wu; Donglin Wang. A Global Path Planning Algorithm for Robots using Reinforcement Learning. In: Proceedings of IEEE International Conference on Robotics and Biomimetics (ROBIO); 6-8 Dec; Dali, China. IEEE; 2019. p. 1693–1698.

Syed Irfan Ali Meerza; Moinul Islam; Md. Mohiuddin Uzzal. Q-Learning Based Particle Swarm Optimization Algorithm for Optimal Path Planning of Swarm of Mobile Robots. In: Proceedings of 1st International Conference on Advances in Science, Engineering and Robotics Technology; 03-05 May; Dhaka, Bangladesh. IEEE; 2019. p. 1–5.

Chutian Sun. A Study of Solving Traveling Salesman Problem with Genetic Algorithm. In: 9th International Conference on Industrial Technology and Management (ICITM); 11-13 Feb; Oxford, UK. IEEE; 2020. p. 307-311.

Ameera Jaradat, Bara’ah Matalkeh, Waed Diabat. Solving Traveling Salesman Problem using Firefly algorithm and K-means Clustering. In: Proceedings of IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT); 09-11 Apr; Amman, Jordan. IEEE; 2019. p. 586-589. DOI:




How to Cite

Pratiba KR, Ridhanya S, Ridhisha J, Hemashree P. Integrated Q-Learning with Firefly Algorithm for Transportation Problems. EAI Endorsed Trans Energy Web [Internet]. 2024 Feb. 6 [cited 2024 Feb. 22];11. Available from: